Obesity remains a substantial public health problem in the United States. Despite the initial effectiveness of behavioral treatments for weight loss, long-term maintenance of weight loss after the end of these treatments remains poor. Current interventions to improve long-term weight loss maintenance have statistically significant but only modest impact on weight regain, and none have been able to change the pattern of weight regain typically observed after treatment ends. These results suggest that new approaches to long-term weight loss maintenance are needed. Treatment approaches that adapt to participants' progress and that deliver targeted intervention components at times when participants are at high-risk for weight regain may be beneficial, and may help accommodate the substantial individual variability typically observed in response to intervention. The foundational evidence needed to develop these treatments, however, including when individuals regain weight and what factors predict this regain, is not available. We propose to conduct secondary analyses on a rich longitudinal dataset including data collected from a 3- month Internet-based behavioral weight loss intervention followed by a 9-month no-contact maintenance period. Seventy-five participants (mean age=50.8y, BMI=31.2, 69 percent Female, 84 percent Caucasian) provided daily weight measurements (using smart scales that sent weights directly to our research center via the cellular network) and weekly self-report of behavioral adherence (self-monitoring of weight, caloric intake, and physical activity) and psychological factors that have been hypothesized to affect weight loss maintenance (e.g., stress, hunger, boredom with goal-related eating and activity behaviors, competing priorities). During intervention, participant lost an average (SD) of -6.264.88 percent of baseline weight, followed by a mean weight regain of 2.984.55 percent from Month 3 to 12 (51 percent met clinical cut-offs for significant weight regain at 12 months). Adherence to daily and weekly data collection was high. The weight dataset currently includes approximately 23,500 weight values representing 86 percent of potential dates measured. Average adherence to weekly self-report measures was 72 percent over the 52 weeks. These data provide a unique opportunity to identify when an individual's weight trajectory shifts from weight loss to regain (identified by an inflection poin) and to characterize the high-risk periods prior to these points (the pattern of weight change, behavioral adherence and psychological factors from the week prior that predict these inflection points), allowing us to develop algorithms to predict when an individual is approaching a high-risk time for weight regain. These results have clear treatment implications as they will provide the empirical foundation for a future adaptive intervention for weight loss maintenance, which could provide targeted, behavioral intervention strategies at times when individuals are at high risk for weight regain.